
import numpy as np
import pandas as pd

dataset = pd.read_csv('data.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , : 3].values

from sklearn.perprocessing import Imputer
imputer = Imputer(missing_values = "NAN",strategy = "mean", axis = 0)
imputer = Imputer.fit(X[ : , 1 :3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])

from sklearn.perprocessing import LabelEncoder , OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0]=labelencoder_X.fit_transform(X[ : , 0])

onehotencoder = OneHotEncoder(categorical_features= [0] )
X = onehotencoder.fix_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)

from sklearn.model_seletion import train_test_split
X_train,X_test,Y_train,Y_test = train_test_split(X , Y ,test_size=0.2,random_state=0)

from sklearn.perprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

